A single carbon footprint is a valuable tool. It shows failure in a system within a year. No matter how ambitious your goals might be, you get to see where you’re spending carbon where’d you rather not. Old fleet vehicles? Inefficient buildings? Refrigerants for old HVAC systems? The CO2e to dollar translation is the essential step.*
*If/when there is a price on carbon, this step could be even more consequential. You’ll have first cost, lifecycle cost, and carbon cost:)
Carbon footprints take time to pull together, especially the first one. The core metrics aren’t that challenging; it’s usually finding who the data owner is and getting it from them. Once you have a rhythm established, annual updates are more comfortable and straightforward.
The value of carbon footprints compounds over time. No matter how you get to years’ worth of carbon footprints, it tells the story of the organization and the sustainability team. What you’re looking for overtime is trends, in particular, their direction. A critical step for carbon footprints over time is normalization. You want to make sure you’re comparing proverbial apples to apples.
Big picture, you want to make sure you’re normalizing towards metrics that align with your organization’s key metrics. This could be headcount, square footage, fleets, vehicle miles traveled, data transferred, etc., or all of the above. The resolution you pursue for normalization depends on both your resources and the value of resolution. For example, weather normalization is beneficial for building performance. A building reacting to a frigid winter or a scorching summer is a worthwhile consideration for a property management group. This same data might be much less important for a business that leases its space.
The most critical thing you’re trying to understand in this analysis is why. Why are some metrics trending up? Down? Neutral? Are these outcomes correlated with anything? Is the relationship causal? For me, many of these questions bubble up during analysis.
The hardest part of this whole process is relating the outcomes in a meaningful way. Specifically, there are times when markets or policy is outpacing a sustainability department. A timely fleet upgrade might look like a big improvement when, in reality, it was prescribed. This can be a hard pill to swallow.
To share a personal story, I led a carbon footprint for a large organization from an operations perspective. It was a carbon footprint for a campus. What we saw in the data is substantial growth across almost every metric and a declining carbon footprint. Sustainability FTW! Right? Unfortunately, no.
Over the ten-year life of the organizations’ sustainability department campus had grown by thousands of square feet, there were numerous renovations and demolitions. The grid had transformed from mostly coal to substantial proportions of natural gas and increasing renewables. These are huge levers within a carbon footprint. Replacing a code building built in the 1950s with one built in the 2000s is a quantum leap forward. Scaling this change across hundreds of thousands of square feet, nevermind countless minor asset upgrades, add up. In addition to efficiency gains, natural gas has roughly half the greenhouse impact of coal (which is a hot debate itself), renewables are at least 20 times less damaging than coal. Changes in the market represented the bulk (99%) of positive outcomes. This news did not go over well.
Does it make sense that the market and policies were more effective across these metrics than a stereotypical sustainability department? Yes. Is this easy to accept or justify? Definitely not.
Wrapping this story up. As unpopular as it was to share that building code and a cleaner grid did more for carbon footprint than existing programs built trust with decision-makers. The data and analysis aligned with the narratives of the budget office, operations, and even donors. Further, it isn’t surprising that things like double-sided printing, transportation challenges, and game-day recycling aren’t moving the needle. Beyond the failure to separate correlation from causation, this sustainability department missed out on years of high-leverage work based on metrics they were actively tracking. Oof.
There are a lot of ways to get here:
- Your organization is religious about carbon footprints, and the raw data is easily accessible.
- Your organization has a patchier history of carbon footprints and a
- You’re currently working through the first-ever carbon footprint.
With the caveat that I’m experienced with carbon footprints but no expert, this is what I think good carbon footprint hygiene looks like:
- Up to date carbon footprint for your requisite fiscal year (FY) or calendar year (CY) – do whatever your budget team does.
- Depending on what’s important to you and the bodies you report to, Scope 1, Scope 2, and elements of Scope 3 are valuable. Scope 1 and Scope 2 are the bare minimum.
- History of full and partial carbon footprints
- Raw data files supporting all key metrics
- A table of assumptions or approaches to normalizing data
- Macro: Inflation, fuel prices,
- Micro: Organizational growth/change (personnel, square feet, fleet, etc.)
- Any commentary, feedback, or anecdotal analysis
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